mirror of
https://github.com/guoyww/AnimateDiff.git
synced 2025-12-16 16:38:01 +01:00
fix gradio
This commit is contained in:
Yuwei
316
app.py
316
app.py
@@ -17,16 +17,17 @@ from transformers import CLIPTextModel, CLIPTokenizer
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from animatediff.models.unet import UNet3DConditionModel
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from animatediff.pipelines.pipeline_animation import AnimationPipeline
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from animatediff.utils.util import save_videos_grid
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from animatediff.utils.util import save_videos_grid, load_weights, auto_download, MOTION_MODULES, BACKUP_DREAMBOOTH_MODELS
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from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
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from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora
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import pdb
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sample_idx = 0
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sample_idx = 0
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scheduler_dict = {
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"DDIM": DDIMScheduler,
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"Euler": EulerDiscreteScheduler,
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"PNDM": PNDMScheduler,
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"DDIM": DDIMScheduler,
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}
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css = """
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@@ -38,142 +39,160 @@ css = """
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}
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"""
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PRETRAINED_SD = "runwayml/stable-diffusion-v1-5"
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default_motion_module = "v3_sd15_mm.ckpt"
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default_inference_config = "configs/inference/inference-v3.yaml"
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default_dreambooth_model = "realisticVisionV60B1_v51VAE.safetensors"
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default_prompt = "b&w photo of 42 y.o man in black clothes, bald, face, half body, body, high detailed skin, skin pores, coastline, overcast weather, wind, waves, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3"
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default_n_prompt = "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
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default_seed = 8893659352891878017
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device = "cuda" if torch.cuda.is_available() else "cpu"
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class AnimateController:
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def __init__(self):
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# config dirs
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self.basedir = os.getcwd()
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self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
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self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
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self.basedir = os.getcwd()
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self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
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self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
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self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
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self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
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self.savedir_sample = os.path.join(self.savedir, "sample")
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self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
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self.savedir_sample = os.path.join(self.savedir, "sample")
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os.makedirs(self.savedir, exist_ok=True)
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self.stable_diffusion_list = []
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self.motion_module_list = []
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self.personalized_model_list = []
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self.refresh_stable_diffusion()
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self.refresh_motion_module()
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self.refresh_personalized_model()
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self.stable_diffusion_list = [PRETRAINED_SD]
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self.motion_module_list = MOTION_MODULES
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self.personalized_model_list = BACKUP_DREAMBOOTH_MODELS
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# config models
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self.tokenizer = None
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self.text_encoder = None
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self.vae = None
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self.unet = None
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self.pipeline = None
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self.lora_model_state_dict = {}
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self.pipeline = None
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# self.lora_model_state_dict = {}
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self.inference_config = OmegaConf.load("configs/inference/inference.yaml")
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self.refresh_stable_diffusion()
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self.refresh_personalized_model()
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# default setting
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self.update_pipeline(
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stable_diffusion_dropdown=PRETRAINED_SD,
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motion_module_dropdown=default_motion_module,
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base_model_dropdown=default_dreambooth_model,
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sampler_dropdown="DDIM",
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)
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def refresh_stable_diffusion(self):
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self.stable_diffusion_list = glob(os.path.join(self.stable_diffusion_dir, "*/"))
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def refresh_motion_module(self):
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motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
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self.motion_module_list = [os.path.basename(p) for p in motion_module_list]
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self.stable_diffusion_list = [PRETRAINED_SD] + glob(os.path.join(self.stable_diffusion_dir, "*/"))
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def refresh_personalized_model(self):
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personalized_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
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self.personalized_model_list = [os.path.basename(p) for p in personalized_model_list]
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self.personalized_model_list = BACKUP_DREAMBOOTH_MODELS + [os.path.basename(p) for p in personalized_model_list if os.path.basename(p) not in BACKUP_DREAMBOOTH_MODELS]
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def update_stable_diffusion(self, stable_diffusion_dropdown):
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self.tokenizer = CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer")
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self.text_encoder = CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder").cuda()
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self.vae = AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae").cuda()
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self.unet = UNet3DConditionModel.from_pretrained_2d(stable_diffusion_dropdown, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
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return gr.Dropdown.update()
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def update_motion_module(self, motion_module_dropdown):
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if self.unet is None:
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gr.Info(f"Please select a pretrained model path.")
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return gr.Dropdown.update(value=None)
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else:
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motion_module_dropdown = os.path.join(self.motion_module_dir, motion_module_dropdown)
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motion_module_state_dict = torch.load(motion_module_dropdown, map_location="cpu")
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missing, unexpected = self.unet.load_state_dict(motion_module_state_dict, strict=False)
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assert len(unexpected) == 0
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return gr.Dropdown.update()
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def update_base_model(self, base_model_dropdown):
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if self.unet is None:
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gr.Info(f"Please select a pretrained model path.")
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return gr.Dropdown.update(value=None)
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else:
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base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
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base_model_state_dict = {}
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with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
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for key in f.keys():
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base_model_state_dict[key] = f.get_tensor(key)
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converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config)
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self.vae.load_state_dict(converted_vae_checkpoint)
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converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_model_state_dict, self.unet.config)
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self.unet.load_state_dict(converted_unet_checkpoint, strict=False)
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self.text_encoder = convert_ldm_clip_checkpoint(base_model_state_dict)
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return gr.Dropdown.update()
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def update_lora_model(self, lora_model_dropdown):
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lora_model_dropdown = os.path.join(self.personalized_model_dir, lora_model_dropdown)
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self.lora_model_state_dict = {}
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if lora_model_dropdown == "none": pass
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else:
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with safe_open(lora_model_dropdown, framework="pt", device="cpu") as f:
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for key in f.keys():
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self.lora_model_state_dict[key] = f.get_tensor(key)
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return gr.Dropdown.update()
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def animate(
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# for dropdown update
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def update_pipeline(
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self,
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stable_diffusion_dropdown,
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motion_module_dropdown,
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base_model_dropdown,
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lora_alpha_slider,
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prompt_textbox,
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negative_prompt_textbox,
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sampler_dropdown,
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sample_step_slider,
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width_slider,
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length_slider,
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height_slider,
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cfg_scale_slider,
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seed_textbox
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):
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if self.unet is None:
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raise gr.Error(f"Please select a pretrained model path.")
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if motion_module_dropdown == "":
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raise gr.Error(f"Please select a motion module.")
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if base_model_dropdown == "":
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raise gr.Error(f"Please select a base DreamBooth model.")
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base_model_dropdown="",
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lora_model_dropdown="none",
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lora_alpha_dropdown="0.6",
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sampler_dropdown="DDIM",
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):
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if "v2" in motion_module_dropdown:
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inference_config = "configs/inference/inference-v2.yaml"
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elif "v3" in motion_module_dropdown:
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inference_config = "configs/inference/inference-v3.yaml"
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else:
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inference_config = "configs/inference/inference-v1.yaml"
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if is_xformers_available(): self.unet.enable_xformers_memory_efficient_attention()
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unet = UNet3DConditionModel.from_pretrained_2d(
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stable_diffusion_dropdown, subfolder="unet",
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unet_additional_kwargs=OmegaConf.load(inference_config).unet_additional_kwargs
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)
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if is_xformers_available() and torch.cuda.is_available():
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unet.enable_xformers_memory_efficient_attention()
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noise_scheduler_cls = scheduler_dict[sampler_dropdown]
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noise_scheduler_kwargs = OmegaConf.load(inference_config).noise_scheduler_kwargs
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if noise_scheduler_cls == EulerDiscreteScheduler:
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noise_scheduler_kwargs.pop("steps_offset")
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noise_scheduler_kwargs.pop("clip_sample")
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elif noise_scheduler_cls == PNDMScheduler:
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noise_scheduler_kwargs.pop("clip_sample")
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pipeline = AnimationPipeline(
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vae=self.vae, text_encoder=self.text_encoder, tokenizer=self.tokenizer, unet=self.unet,
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scheduler=scheduler_dict[sampler_dropdown](**OmegaConf.to_container(self.inference_config.noise_scheduler_kwargs))
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).to("cuda")
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if self.lora_model_state_dict != {}:
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pipeline = convert_lora(pipeline, self.lora_model_state_dict, alpha=lora_alpha_slider)
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unet=unet,
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vae=AutoencoderKL.from_pretrained(stable_diffusion_dropdown, subfolder="vae"),
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text_encoder=CLIPTextModel.from_pretrained(stable_diffusion_dropdown, subfolder="text_encoder"),
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tokenizer=CLIPTokenizer.from_pretrained(stable_diffusion_dropdown, subfolder="tokenizer"),
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scheduler=noise_scheduler_cls(**noise_scheduler_kwargs),
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)
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pipeline.to("cuda")
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pipeline = load_weights(
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pipeline,
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motion_module_path=os.path.join(self.motion_module_dir, motion_module_dropdown),
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dreambooth_model_path=os.path.join(self.personalized_model_dir, base_model_dropdown) if base_model_dropdown != "" else "",
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lora_model_path=os.path.join(self.personalized_model_dir, lora_model_dropdown) if lora_model_dropdown != "none" else "",
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lora_alpha=float(lora_alpha_dropdown),
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)
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if seed_textbox != -1 and seed_textbox != "": torch.manual_seed(int(seed_textbox))
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else: torch.seed()
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pipeline.to(device)
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self.pipeline = pipeline
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print("done.")
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return gr.Dropdown.update()
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def update_pipeline_alpha(
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self,
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stable_diffusion_dropdown,
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motion_module_dropdown,
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base_model_dropdown="",
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lora_model_dropdown="none",
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lora_alpha_dropdown="0.6",
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sampler_dropdown="DDIM",
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):
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if lora_model_dropdown == "none":
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return gr.Slider.update()
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self.update_pipeline(
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stable_diffusion_dropdown=stable_diffusion_dropdown,
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motion_module_dropdown=motion_module_dropdown,
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base_model_dropdown=base_model_dropdown,
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lora_model_dropdown=lora_model_dropdown,
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lora_alpha_dropdown=lora_alpha_dropdown,
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sampler_dropdown=sampler_dropdown,
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)
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return gr.Slider.update()
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@torch.no_grad()
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def animate(
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self,
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prompt_textbox,
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negative_prompt_textbox,
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sampler_dropdown,
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sample_step_slider,
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width_slider,
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length_slider,
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height_slider,
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cfg_scale_slider,
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seed_textbox,
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):
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if int(seed_textbox) != -1:
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torch.manual_seed(int(seed_textbox))
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else:
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torch.seed()
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seed = torch.initial_seed()
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sample = pipeline(
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sample = self.pipeline(
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prompt_textbox,
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negative_prompt = negative_prompt_textbox,
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negative_prompt = negative_prompt_textbox,
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num_inference_steps = sample_step_slider,
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guidance_scale = cfg_scale_slider,
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width = width_slider,
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height = height_slider,
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video_length = length_slider,
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guidance_scale = cfg_scale_slider,
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width = width_slider,
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height = height_slider,
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video_length = length_slider,
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).videos
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save_sample_path = os.path.join(self.savedir_sample, f"{sample_idx}.mp4")
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@@ -190,6 +209,7 @@ class AnimateController:
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"video_length": length_slider,
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"seed": seed
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}
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json_str = json.dumps(sample_config, indent=4)
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with open(os.path.join(self.savedir, "logs.json"), "a") as f:
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f.write(json_str)
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@@ -205,51 +225,39 @@ def ui():
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with gr.Blocks(css=css) as demo:
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gr.Markdown(
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"""
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# [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://arxiv.org/abs/2307.04725)
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Yuwei Guo, Ceyuan Yang*, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai (*Corresponding Author)<br>
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[Arxiv Report](https://arxiv.org/abs/2307.04725) | [Project Page](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
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# AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
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Yuwei Guo, Ceyuan Yang✝, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, Bo Dai (✝Corresponding Author)<br>
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[Paper](https://arxiv.org/abs/2307.04725) | [Webpage](https://animatediff.github.io/) | [Github](https://github.com/guoyww/animatediff/)
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"""
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)
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with gr.Column(variant="panel"):
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gr.Markdown(
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"""
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### 1. Model checkpoints (select pretrained model path first).
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### 1. Model Checkpoints
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"""
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)
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with gr.Row():
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stable_diffusion_dropdown = gr.Dropdown(
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label="Pretrained Model Path",
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choices=controller.stable_diffusion_list,
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value=PRETRAINED_SD,
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interactive=True,
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)
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stable_diffusion_dropdown.change(fn=controller.update_stable_diffusion, inputs=[stable_diffusion_dropdown], outputs=[stable_diffusion_dropdown])
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stable_diffusion_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
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def update_stable_diffusion():
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controller.refresh_stable_diffusion()
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return gr.Dropdown.update(choices=controller.stable_diffusion_list)
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stable_diffusion_refresh_button.click(fn=update_stable_diffusion, inputs=[], outputs=[stable_diffusion_dropdown])
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with gr.Row():
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motion_module_dropdown = gr.Dropdown(
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label="Select motion module",
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choices=controller.motion_module_list,
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value=default_motion_module,
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interactive=True,
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)
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motion_module_dropdown.change(fn=controller.update_motion_module, inputs=[motion_module_dropdown], outputs=[motion_module_dropdown])
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motion_module_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
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def update_motion_module():
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controller.refresh_motion_module()
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return gr.Dropdown.update(choices=controller.motion_module_list)
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motion_module_refresh_button.click(fn=update_motion_module, inputs=[], outputs=[motion_module_dropdown])
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base_model_dropdown = gr.Dropdown(
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label="Select base Dreambooth model (required)",
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choices=controller.personalized_model_list,
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value=default_dreambooth_model,
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interactive=True,
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)
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base_model_dropdown.change(fn=controller.update_base_model, inputs=[base_model_dropdown], outputs=[base_model_dropdown])
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lora_model_dropdown = gr.Dropdown(
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label="Select LoRA model (optional)",
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@@ -257,18 +265,24 @@ def ui():
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value="none",
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interactive=True,
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)
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lora_model_dropdown.change(fn=controller.update_lora_model, inputs=[lora_model_dropdown], outputs=[lora_model_dropdown])
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lora_alpha_slider = gr.Slider(label="LoRA alpha", value=0.8, minimum=0, maximum=2, interactive=True)
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lora_alpha_dropdown = gr.Dropdown(
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label="LoRA alpha",
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choices=["0.", "0.2", "0.4", "0.6", "0.8", "1.0"],
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value="0.6",
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interactive=True,
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)
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personalized_refresh_button = gr.Button(value="\U0001F503", elem_classes="toolbutton")
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def update_personalized_model():
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controller.refresh_stable_diffusion()
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controller.refresh_personalized_model()
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return [
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gr.Dropdown.update(choices=controller.stable_diffusion_list),
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gr.Dropdown.update(choices=controller.personalized_model_list),
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gr.Dropdown.update(choices=["none"] + controller.personalized_model_list)
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]
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personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[base_model_dropdown, lora_model_dropdown])
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personalized_refresh_button.click(fn=update_personalized_model, inputs=[], outputs=[stable_diffusion_dropdown, base_model_dropdown, lora_model_dropdown])
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|
||||
with gr.Column(variant="panel"):
|
||||
gr.Markdown(
|
||||
@@ -276,37 +290,39 @@ def ui():
|
||||
### 2. Configs for AnimateDiff.
|
||||
"""
|
||||
)
|
||||
|
||||
prompt_textbox = gr.Textbox(label="Prompt", lines=2)
|
||||
negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2)
|
||||
|
||||
prompt_textbox = gr.Textbox(label="Prompt", lines=2, value=default_prompt)
|
||||
negative_prompt_textbox = gr.Textbox(label="Negative prompt", lines=2, value=default_n_prompt)
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
|
||||
sampler_dropdown = gr.Dropdown(label="Sampling method", choices=list(scheduler_dict.keys()), value=list(scheduler_dict.keys())[0])
|
||||
sample_step_slider = gr.Slider(label="Sampling steps", value=25, minimum=10, maximum=100, step=1)
|
||||
|
||||
width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64)
|
||||
height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64)
|
||||
length_slider = gr.Slider(label="Animation length", value=16, minimum=8, maximum=24, step=1)
|
||||
cfg_scale_slider = gr.Slider(label="CFG Scale", value=7.5, minimum=0, maximum=20)
|
||||
width_slider = gr.Slider(label="Width", value=512, minimum=256, maximum=1024, step=64)
|
||||
height_slider = gr.Slider(label="Height", value=512, minimum=256, maximum=1024, step=64)
|
||||
length_slider = gr.Slider(label="Animation length (default: 16)", value=16, minimum=8, maximum=24, step=1)
|
||||
cfg_scale_slider = gr.Slider(label="CFG Scale", value=8.0, minimum=0, maximum=20)
|
||||
|
||||
with gr.Row():
|
||||
seed_textbox = gr.Textbox(label="Seed", value=-1)
|
||||
seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
|
||||
seed_textbox = gr.Textbox(label="Seed (-1 for random seed)", value=default_seed)
|
||||
seed_button = gr.Button(value="\U0001F3B2", elem_classes="toolbutton")
|
||||
seed_button.click(fn=lambda: gr.Textbox.update(value=random.randint(1, 1e8)), inputs=[], outputs=[seed_textbox])
|
||||
|
||||
generate_button = gr.Button(value="Generate", variant='primary')
|
||||
|
||||
result_video = gr.Video(label="Generated Animation", interactive=False)
|
||||
|
||||
# update method
|
||||
stable_diffusion_dropdown.change(fn=controller.update_pipeline, inputs=[stable_diffusion_dropdown, motion_module_dropdown, base_model_dropdown, lora_model_dropdown, lora_alpha_dropdown, sampler_dropdown], outputs=[stable_diffusion_dropdown])
|
||||
motion_module_dropdown.change(fn=controller.update_pipeline, inputs=[stable_diffusion_dropdown, motion_module_dropdown, base_model_dropdown, lora_model_dropdown, lora_alpha_dropdown, sampler_dropdown], outputs=[motion_module_dropdown])
|
||||
base_model_dropdown.change(fn=controller.update_pipeline, inputs=[stable_diffusion_dropdown, motion_module_dropdown, base_model_dropdown, lora_model_dropdown, lora_alpha_dropdown, sampler_dropdown], outputs=[base_model_dropdown])
|
||||
lora_model_dropdown.change(fn=controller.update_pipeline, inputs=[stable_diffusion_dropdown, motion_module_dropdown, base_model_dropdown, lora_model_dropdown, lora_alpha_dropdown, sampler_dropdown], outputs=[lora_model_dropdown])
|
||||
lora_alpha_dropdown.change(fn=controller.update_pipeline_alpha, inputs=[stable_diffusion_dropdown, motion_module_dropdown, base_model_dropdown, lora_model_dropdown, lora_alpha_dropdown, sampler_dropdown], outputs=[lora_alpha_dropdown])
|
||||
|
||||
generate_button.click(
|
||||
fn=controller.animate,
|
||||
inputs=[
|
||||
stable_diffusion_dropdown,
|
||||
motion_module_dropdown,
|
||||
base_model_dropdown,
|
||||
lora_alpha_slider,
|
||||
prompt_textbox,
|
||||
negative_prompt_textbox,
|
||||
sampler_dropdown,
|
||||
|
||||
Reference in New Issue
Block a user